Introduction
To validate the cellular subpopulations identified by flowSpy, we used a 13-marker panel mass cytometry dataset of healthy human bone marrow. This dataset was generated from Bendall et al [1] and completed quality control by Herring et al [2], which could be downloaded from FlowRepository database [3] (https://flowrepository.org/id/FR-FCM-ZY9R). The aim of this use case was to identify the cellular subpopulations and construct a tree-shaped trajectory, which could reveal the human hematopoietic differentiation hierarchy.
This tutorial contains key steps of flowSpy base workflow, including how to build an FSPY object, how to run clustering and dimensionality reduction, how to build a tree based on minimum spanning tree (MST) algorithm, how to run pseudotime and how to identify intermediate state cells.
Workflow
# Loading packages
suppressMessages({
library(ggplot2)
library(flowCore)
library(pheatmap)
library(flowSpy)
library(stringr)
})
#########################
# Read Flow Cytometry Data
# It can be downloaded via `git clone https://github.com/ytdai/flowSpy-dataset.git`
# fcs.path musted be modified based on the download directory from GitHub
fcs.path <- "../../flowSpy-dataset/FCS/usecase1_2/"
fcs.file <- paste0(fcs.path, "FR-FCM-ZY9R-Bone_Marrow_cytof.fcs")
###########################################
# Get the expression matrix from FCS file
###########################################
# Solution 1
# Read FCS data via flowCore::read.FCS
# Expression data matrix from this method need to
# be performed compensation adjustment and transformation
# manually using flowCore
cytof.data <- flowCore::read.FCS(filename = fcs.file)
# show elements in mass cytometry data
cytof.data## flowFrame object 'FR-FCM-ZY9R-Bone_Marrow_cytof.fcs'
## with 236187 cells and 13 observables:
## name desc range minRange maxRange
## $P1 CD3 <NA> 16384 -36.47161 16383
## $P2 CD45RA <NA> 16384 -49.93872 16383
## $P3 CD19 <NA> 16384 -85.81519 16383
## $P4 CD11b <NA> 16384 -50.06744 16383
## $P5 CD4 <NA> 16384 -22.94810 16383
## $P6 CD8 <NA> 16384 -81.21340 16383
## $P7 CD34 <NA> 16384 -52.97938 16383
## $P8 CD20 <NA> 16384 -78.41647 16383
## $P9 CD33 <NA> 16384 -27.77563 16383
## $P10 CD123 <NA> 16384 -51.42798 16383
## $P11 CD38 <NA> 16384 -77.72259 16383
## $P12 CD90 <NA> 16384 -31.92096 16383
## $P13 CD45 <NA> 16384 -34.86934 16383
## 95 keywords are stored in the 'description' slot# fetching expression data
exp.data <- cytof.data@exprs
# Solution 2
# Read FCS data via flowSpy::runExprsExtract
# ** This solution is recommended
# ** Use case 1 and 2 follow this solution
exp.data <- runExprsExtract(fcs.file, showDesc = FALSE, transformMethod = "autoLgcl")
# Fetching CD markers
markers <- colnames(exp.data)
markers.idx <- match(markers, colnames(exp.data))
# Build an FSPY object
# If you don't want to see the running log information, set verbose FALSE
# If there is only one case in your analysis workflow, you can just set stage "D0"
meta.data <- data.frame(cell = rownames(exp.data),
stage = "D0")
fspy <- createFSPY(raw.data = exp.data, markers = markers,
meta.data = meta.data,
normalization.method = "none",
verbose = T)## 2019-09-09 22:08:06 [INFO] Number of cells in processing: 236187## 2019-09-09 22:08:06 [INFO] rownames of meta.data and raw.data will be named using column cell## 2019-09-09 22:08:06 [INFO] Index of markers in processing## 2019-09-09 22:08:06 [INFO] Creating FSPY object.## 2019-09-09 22:08:06 [INFO] No normalization and transformation## 2019-09-09 22:08:06 [INFO] Build FSPY object succeed# Cluster cells by SOM algorithm
# Set random seed to make results reproducible
set.seed(6)
fspy <- runCluster(fspy, cluster.method = "som", xdim = 10, ydim = 10, verbose = T)## 2019-09-09 22:08:06 [INFO] Calculating FlowSOM.## 2019-09-09 22:08:11 [INFO] Calculating FlowSOM completed.# Cluster based downsampling
# The total cell number is 236,187, and we can just keep 10% cells to reduce
# computation load and improve computation time.
# Downsampling by setting downsampleing.size 0.1
set.seed(1)
fspy <- processingCluster(fspy, perplexity = 5,downsampling.size = 0.1)
# Now only 23,664 cells are enrolled in the dimensionality reduction
fspy## FSPY Information:
## Input cell number: 236187 cells
## Enroll marker number: 13 markers
## Cells after downsampling: 23664 markers## 2019-09-09 22:08:19 [INFO] Calculating PCA.## 2019-09-09 22:08:19 [INFO] Calculating PCA completed.# run t-Distributed Stochastic Neighbor Embedding (tSNE)
set.seed(1)
fspy <- runTSNE(fspy, dims = 2, verbose = T)## 2019-09-09 22:08:19 [INFO] Calculating tSNE.## 2019-09-09 22:10:52 [INFO] Calculating tSNE completed.## 2019-09-09 22:10:52 [INFO] Calculating Diffusion Map.## 2019-09-09 22:10:52 [INFO] Destiny determined an optimal global sigma: 1.449## 2019-09-09 22:13:27 [INFO] Calculating Diffusion Map completed## 2019-09-09 22:13:27 [INFO] Calculating Umap.## 2019-09-09 22:17:05 [INFO] Calculating Umap.###########################################
# This is visualization module
###########################################
# Plot 2D PCA. And cells are colored by CD3 expression
plot2D(fspy, item.use = c("PC_1", "PC_2"), color.by = "CD3",
alpha = 1, main = "PCA", category = "numeric") +
scale_colour_gradientn(colors = c("#00599F","#EEEEEE","#FF3222"))
# Plot 2D tSNE. And cells are colored by CD3 expression
plot2D(fspy, item.use = c("tSNE_1", "tSNE_2"), color.by = "CD3",
alpha = 1, main = "tSNE", category = "numeric") +
scale_colour_gradientn(colors = c("#00599F","#EEEEEE","#FF3222"))
# Plot 2D diffusion maps. And cells are colored by CD3 expression
plot2D(fspy, item.use = c("DC_1", "DC_2"), color.by = "CD3",
alpha = 1, main = "diffusion maps", category = "numeric") +
scale_colour_gradientn(colors = c("#00599F","#EEEEEE","#FF3222"))
# Plot 2D UMAP. And cells are colored by CD3 expression
plot2D(fspy, item.use = c("UMAP_1", "UMAP_2"), color.by = "CD3",
alpha = 1, main = "UMAP", category = "numeric") +
scale_colour_gradientn(colors = c("#00599F","#EEEEEE","#FF3222"))
# Plot 2D tSNE. And cells are colored by cluster id
plot2D(fspy, item.use = c("tSNE_1", "tSNE_2"), color.by = "cluster.id",
alpha = 1, main = "tSNE", category = "categorical", show.cluser.id = T)
# Plot 2D UMAP. And cells are colored by cluster id
plot2D(fspy, item.use = c("UMAP_1", "UMAP_2"), color.by = "cluster.id",
alpha = 1, main = "UMAP", category = "categorical", show.cluser.id = T)
# Plot 3D UMAP. And cells are colored by CD45RA markers expression
plot3D(fspy, item.use = c("DC_1", "DC_2", "DC_3"), color.by = "CD3",
main = "diffusion maps CD3", category = "numeric", size = 0.2,
color.theme = c("#00599F","#EEEEEE","#FF3222"))
###########################################
# Trajectory
###########################################
# flowSpy provides five method to build the tree-shaped trajectory:
# 1. Raw expression matrix
# 2. PCA
# 3. tSNE
# 4. Diffusion maps
# 5. UMAP
# 1. Raw expression matrix
fspy <- buildTree(fspy, dim.type = "raw", verbose = T)## 2019-09-09 22:17:18 [INFO] Calculating buildTree.## 2019-09-09 22:17:18 [INFO] The log data will be used to calculate trajectory## 2019-09-09 22:17:19 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:19 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
## 2019-09-09 22:17:20 [INFO] Calculating buildTree.## 2019-09-09 22:17:20 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:20 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
## 2019-09-09 22:17:21 [INFO] Calculating buildTree.## 2019-09-09 22:17:21 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:21 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
## 2019-09-09 22:17:22 [INFO] Calculating buildTree.## 2019-09-09 22:17:22 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:22 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
## 2019-09-09 22:17:23 [INFO] Calculating buildTree.## 2019-09-09 22:17:23 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:23 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
# The topology of a trajectory is mainly based on the interrelation
# of cell clusters, coordinates and dimensions, and in use case 1 and
# 2, we use "tsne" to construct the trajectory
fspy <- buildTree(fspy, dim.type = "tsne", dim.use = 1:2, verbose = T)## 2019-09-09 22:17:24 [INFO] Calculating buildTree.## 2019-09-09 22:17:24 [INFO] Initialization for root.cells and leaf cells## 2019-09-09 22:17:24 [INFO] Calculating buildTree completed.# Tree plot
plotTree(fspy, color.by = "CD3", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
plotTree(fspy, color.by = "CD34", show.node.name = T, cex.size = 1) +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
############# Modify branch id
fspy@meta.data$branch.id[fspy@meta.data$cluster.id %in% c(9,25,90,30)] = 12
fspy@meta.data$branch.id[fspy@meta.data$cluster.id %in% c(5,62)] = 6
fspy@meta.data$branch.id[fspy@meta.data$cluster.id %in% c(38,87)] = 3
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(12)] = "HSCs"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(1,2)] = "CD8 T cells"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(4,5)] = "CD4 T cells"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(3,9,11)] = "Megakaryocytic"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(6)] = "NK cells"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(7)] = "B cells"
fspy@meta.data$branch.id[fspy@meta.data$branch.id %in% c(8,10)] = "Myeloid"
# Run differential expressed markers of different branch
diff.info <- runDiff(fspy)
head(diff.info)## logFC AveExpr t P.Value adj.P.Val B
## CD8 1.7827096 1.079246 158.52189 0 0 8554.0978
## CD3 0.8365197 1.096834 71.34905 0 0 2295.5803
## CD45 0.7322583 2.343466 54.20401 0 0 1374.7889
## CD45RA 0.7396298 1.617103 53.30852 0 0 1331.8850
## CD33 -0.9383986 1.796045 -52.36995 0 0 1287.5212
## CD123 -0.5759454 1.105198 -41.20916 0 0 810.0004
## branch.contrast Gene
## CD8 CD8 T cells_vs_other CD8
## CD3 CD8 T cells_vs_other CD3
## CD45 CD8 T cells_vs_other CD45
## CD45RA CD8 T cells_vs_other CD45RA
## CD33 CD8 T cells_vs_other CD33
## CD123 CD8 T cells_vs_other CD123
plotCluster(fspy, item.use = c("tSNE_1", "tSNE_2"), category = "categorical",
size = 100, color.by = "branch.id", show.cluser.id = T) 
# Plot 2D and colored by branch id
plot2D(fspy, item.use = c("tSNE_1", "tSNE_2"), color.by = "branch.id",
alpha = 1, main = "tSNE", category = "categorical", show.cluser.id = F)
plot2D(fspy, item.use = c("PC_1", "PC_2"), color.by = "branch.id",
alpha = 1, main = "PCA", category = "categorical", show.cluser.id = F)
plot2D(fspy, item.use = c("DC_1", "DC_2"), color.by = "branch.id",
alpha = 1, main = "diffusion maps", category = "categorical", show.cluser.id = F)
plot2D(fspy, item.use = c("UMAP_1", "UMAP_2"), color.by = "branch.id",
alpha = 1, main = "UMAP", category = "categorical", show.cluser.id = F)
# plot for clusters
plotCluster(fspy, item.use = c("PC_1", "PC_2"), category = "numeric",
size = 10, color.by = "CD3", main = "PCA of clusters") +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
plotCluster(fspy, item.use = c("tSNE_1", "tSNE_2"), category = "numeric",
size = 100, color.by = "CD3", main = "tSNE of clusters") +
scale_colour_gradientn(colors = c("#00599F", "#EEEEEE", "#FF3222"))
plotCluster(fspy, item.use = c("tSNE_1", "tSNE_2"), category = "categorical",
size = 100, color.by = "branch.id", show.cluser.id = T)
###########################################
# Pseudotime
###########################################
# Set HSPCs as root cells
fspy <- defRootCells(fspy, root.cells = c(9,25,90,30), verbose = T)## 2019-09-09 22:17:37 [INFO] 570 cells will be added to root.cells .## 2019-09-09 22:17:37 [INFO] Calculating Pseudotime.## 2019-09-09 22:17:37 [INFO] Pseudotime exists in meta.data, it will be replaced.## 2019-09-09 22:17:37 [INFO] The log data will be used to calculate trajectory## 2019-09-09 22:19:18 [INFO] Calculating Pseudotime completed.###### Intermediate state cells for CD8 T cells
fspy <- defLeafCells(fspy, leaf.cells = c(23,28,51), verbose = T)## 2019-09-09 22:19:18 [INFO] 650 cells will be added to leaf.cells .## 2019-09-09 22:19:18 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:19:22 [INFO] Generating an adjacency matrix.## 2019-09-09 22:20:42 [INFO] Walk forward.## 2019-09-09 22:20:47 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.CD8T <- fspy@meta.data$traj.value.log
###### Intermediate state cells for CD4 T cells
fspy <- defLeafCells(fspy, leaf.cells = c(52,78,46), verbose = T)## 2019-09-09 22:20:47 [INFO] leaf.cells in FSPY object exist, they will be replaced.## 2019-09-09 22:20:47 [INFO] 1012 cells will be added to leaf.cells .## 2019-09-09 22:20:47 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:20:51 [INFO] Generating an adjacency matrix.## 2019-09-09 22:22:10 [INFO] Walk forward.## 2019-09-09 22:22:19 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.CD4T <- fspy@meta.data$traj.value.log
###### Intermediate state cells for NK cells
fspy <- defLeafCells(fspy, leaf.cells = c(54,48), verbose = T)## 2019-09-09 22:22:19 [INFO] leaf.cells in FSPY object exist, they will be replaced.## 2019-09-09 22:22:19 [INFO] 392 cells will be added to leaf.cells .## 2019-09-09 22:22:19 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:22:22 [INFO] Generating an adjacency matrix.## 2019-09-09 22:23:54 [INFO] Walk forward.## 2019-09-09 22:23:56 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.NK <- fspy@meta.data$traj.value.log
###### Intermediate state cells for B cells
fspy <- defLeafCells(fspy, leaf.cells = c(11,89,94), verbose = T)## 2019-09-09 22:23:56 [INFO] leaf.cells in FSPY object exist, they will be replaced.## 2019-09-09 22:23:56 [INFO] 704 cells will be added to leaf.cells .## 2019-09-09 22:23:56 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:24:00 [INFO] Generating an adjacency matrix.## 2019-09-09 22:25:32 [INFO] Walk forward.## 2019-09-09 22:25:37 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.B <- fspy@meta.data$traj.value.log
###### Intermediate state cells for monocytes and granulocytes
fspy <- defLeafCells(fspy, leaf.cells = c(49,93,42), verbose = T)## 2019-09-09 22:25:37 [INFO] leaf.cells in FSPY object exist, they will be replaced.## 2019-09-09 22:25:38 [INFO] 739 cells will be added to leaf.cells .## 2019-09-09 22:25:38 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:25:40 [INFO] Generating an adjacency matrix.## 2019-09-09 22:26:59 [INFO] Walk forward.## 2019-09-09 22:27:04 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.MY <- fspy@meta.data$traj.value.log
###### Intermediate state cells for megakaryocyte and erythrocyte
fspy <- defLeafCells(fspy, leaf.cells = c(14,31,100,63,98), verbose = T)## 2019-09-09 22:27:04 [INFO] leaf.cells in FSPY object exist, they will be replaced.## 2019-09-09 22:27:04 [INFO] 1583 cells will be added to leaf.cells .## 2019-09-09 22:27:04 [INFO] Calculating walk between root.cells and leaf.cells .## 2019-09-09 22:27:08 [INFO] Generating an adjacency matrix.## 2019-09-09 22:28:36 [INFO] Walk forward.## 2019-09-09 22:28:47 [INFO] Calculating walk completed.fspy@meta.data$traj.value.log.ME <- fspy@meta.data$traj.value.log
# Plot 2D tSNE.
fspy@meta.data$stage <- fspy@meta.data$branch.id
plot2D(fspy, item.use = c("tSNE_1", "tSNE_2"), category = "numeric",
size = 1, color.by = "pseudotime") +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))
# Tree plot
plotTree(fspy, color.by = "pseudotime", cex.size = 1) +
scale_colour_gradientn(colors = c("#F4D31D","#FF3222","#7A06A0"))
plotPseudotimeTraj(fspy, var.cols = T) +
scale_colour_gradientn(colors = c("#F4D31D", "#FF3222","#7A06A0"))
### fetch plot information
plot.meta <- fetchPlotMeta(fspy, markers = markers)
# heatmap for CD8 T cells
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.CD8T > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
# heatmap for CD4 T cells
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.CD4T > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
# heatmap for NK cells
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.NK > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
# heatmap for B cells
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.B > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
# heatmap for monocytes and granulocytes
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.MY > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
# heatmap for megakaryocyte and erythrocyte
plot.meta.sub <- plot.meta[which(plot.meta$traj.value.log.ME > 0), ]
plot.meta.sub <- plot.meta.sub[order(plot.meta.sub$pseudotime), ]
pheatmap(t(plot.meta.sub[, markers]), scale = "row",
cluster_rows = T, cluster_cols = F, cluster_method = "ward.D",
color = colorRampPalette(c("blue","blue","white","red","red"))(100),
fontsize_col = 0.01)
Session information
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringr_1.4.0 flowSpy_1.2.7 igraph_1.2.4.1 pheatmap_1.0.12
## [5] flowCore_1.50.0 ggplot2_3.2.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.4
## [3] RcppEigen_0.3.3.5.0 plyr_1.8.4
## [5] ConsensusClusterPlus_1.48.0 lazyeval_0.2.2
## [7] sp_1.3-1 splines_3.6.1
## [9] BiocParallel_1.18.1 GenomeInfoDb_1.20.0
## [11] sva_3.32.1 digest_0.6.20
## [13] htmltools_0.3.6 gdata_2.18.0
## [15] magrittr_1.5 memoise_1.1.0
## [17] cluster_2.1.0 openxlsx_4.1.0.1
## [19] limma_3.40.6 annotate_1.62.0
## [21] matrixStats_0.54.0 gmodels_2.18.1
## [23] xts_0.11-2 colorspace_1.4-1
## [25] blob_1.2.0 rrcov_1.4-7
## [27] haven_2.1.1 xfun_0.8
## [29] dplyr_0.8.3 crayon_1.3.4
## [31] RCurl_1.95-4.12 jsonlite_1.6
## [33] graph_1.62.0 scatterpie_0.1.2
## [35] genefilter_1.66.0 zeallot_0.1.0
## [37] survival_2.44-1.1 zoo_1.8-6
## [39] glue_1.3.1 polyclip_1.10-0
## [41] gtable_0.3.0 zlibbioc_1.30.0
## [43] XVector_0.24.0 DelayedArray_0.10.0
## [45] car_3.0-3 BiocGenerics_0.30.0
## [47] DEoptimR_1.0-8 abind_1.4-5
## [49] VIM_4.8.0 scales_1.0.0
## [51] mvtnorm_1.0-11 DBI_1.0.0
## [53] ggthemes_4.2.0 Rcpp_1.0.2
## [55] xtable_1.8-4 laeken_0.5.0
## [57] reticulate_1.13 foreign_0.8-72
## [59] bit_1.1-14 proxy_0.4-23
## [61] mclust_5.4.5 FlowSOM_1.16.0
## [63] stats4_3.6.1 tsne_0.1-3
## [65] umap_0.2.2.0 vcd_1.4-4
## [67] RColorBrewer_1.1-2 pkgconfig_2.0.2
## [69] XML_3.98-1.20 farver_1.1.0
## [71] nnet_7.3-12 reshape2_1.4.3
## [73] labeling_0.3 tidyselect_0.2.5
## [75] rlang_0.4.0 AnnotationDbi_1.46.0
## [77] munsell_0.5.0 cellranger_1.1.0
## [79] tools_3.6.1 RSQLite_2.1.2
## [81] ranger_0.11.2 evaluate_0.14
## [83] yaml_2.2.0 knitr_1.24
## [85] bit64_0.9-7 zip_2.0.3
## [87] robustbase_0.93-5 purrr_0.3.2
## [89] RANN_2.6.1 nlme_3.1-141
## [91] compiler_3.6.1 curl_4.0
## [93] e1071_1.7-2 smoother_1.1
## [95] tibble_2.1.3 tweenr_1.0.1
## [97] pcaPP_1.9-73 stringi_1.4.3
## [99] RSpectra_0.15-0 forcats_0.4.0
## [101] lattice_0.20-38 Matrix_1.2-17
## [103] vctrs_0.2.0 pillar_1.4.2
## [105] RUnit_0.4.32 lmtest_0.9-37
## [107] BiocNeighbors_1.2.0 data.table_1.12.2
## [109] bitops_1.0-6 corpcor_1.6.9
## [111] GenomicRanges_1.36.0 R6_2.4.0
## [113] rio_0.5.16 IRanges_2.18.1
## [115] flowUtils_1.48.0 boot_1.3-23
## [117] MASS_7.3-51.4 gtools_3.8.1
## [119] assertthat_0.2.1 destiny_2.14.0
## [121] SummarizedExperiment_1.14.1 withr_2.1.2
## [123] S4Vectors_0.22.0 GenomeInfoDbData_1.2.1
## [125] mgcv_1.8-28 parallel_3.6.1
## [127] hms_0.5.0 grid_3.6.1
## [129] prettydoc_0.3.0 tidyr_0.8.3
## [131] class_7.3-15 rmarkdown_1.14
## [133] rvcheck_0.1.3 carData_3.0-2
## [135] Rtsne_0.15 TTR_0.23-4
## [137] ggforce_0.2.2 scatterplot3d_0.3-41
## [139] Biobase_2.44.0References
Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, et al: Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 2011, 332:687-696.
Herring CA, Banerjee A, McKinley ET, Simmons AJ, Ping J, Roland JT, Franklin JL, Liu Q, Gerdes MJ, Coffey RJ, Lau KS: Unsupervised Trajectory Analysis of Single-Cell RNA-Seq and Imaging Data Reveals Alternative Tuft Cell Origins in the Gut. Cell Syst 2018, 6:37-51 e39.
Spidlen J, Breuer K, Rosenberg C, Kotecha N, Brinkman RR: FlowRepository: a resource of annotated flow cytometry datasets associated with peer-reviewed publications. Cytometry A 2012, 81:727-731.